Modulation Recognition Algorithm Based on Improved LDA and Autoencoders

Authors

Yecai Guo, 1. School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; ;2. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing University of Information Science & Technology, Nanjing 210044, China;
Haoran Zhang, 1. School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; ;

Abstract

Abstract: The traditional modulation recognition algorithms are based on the Gaussian white noise channel, which significantly degrade recognition performance in complex channel conditions. Aiming at this problem, a modulation recognition algorithm based on A-ALDA (Anti-alias Linear Discriminant Analysis) and SSDAE (Stacked Sparse Denoising Autoencoders) is proposed. In this algorithm, A-ALDA algorithm reconstructs signal cumulants feature into new features, which has better separability. The combination of original features and new features is input into SSDAE for classification, and SSADE has the ability to extract key information and resist noise. Simulation results show that recognition accuracy of the proposed algorithm is higher than that of the existing algorithms, and recognition accuracy is improved under the condition of limited signal length and phase and frequency offset interference.

Recommended Citation

Guo, Yecai and Zhang, Haoran (2021) "Modulation Recognition Algorithm Based on Improved LDA and Autoencoders," Journal of System Simulation: Vol. 33: Iss. 2, Article 27.
DOI: 10.16182/j.issn1004731x.joss.19-0326
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss2/27

First Page

494

Revised Date

2019-09-11

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-0326

Last Page

500

CLC

TP391

Recommended Citation

Guo Yecai, Zhang Haoran. Modulation Recognition Algorithm Based on Improved LDA and Autoencoders[J]. Journal of System Simulation, 2021, 33(2): 494-500.

DOI

10.16182/j.issn1004731x.joss.19-0326